Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations140446
Missing cells784
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.1 MiB
Average record size in memory180.1 B

Variable types

DateTime3
Categorical8
Numeric7

Alerts

Carbon emissions estimate num is highly overall correlated with carbon_emission% num and 1 other fieldsHigh correlation
Trip_Type is highly overall correlated with round_trip_durationHigh correlation
carbon_emission% num is highly overall correlated with Carbon emissions estimate numHigh correlation
carrier is highly overall correlated with overhead_binHigh correlation
flight_duration_value is highly overall correlated with layover_countHigh correlation
layover_count is highly overall correlated with flight_duration_valueHigh correlation
overhead_bin is highly overall correlated with Carbon emissions estimate num and 1 other fieldsHigh correlation
price is highly overall correlated with round_trip_durationHigh correlation
round_trip_duration is highly overall correlated with Trip_Type and 1 other fieldsHigh correlation
layover_count is highly imbalanced (67.8%)Imbalance
Holiday is highly imbalanced (86.0%)Imbalance
round_trip_duration has 63316 (45.1%) zerosZeros

Reproduction

Analysis started2024-07-25 23:43:02.675866
Analysis finished2024-07-25 23:43:14.854316
Duration12.18 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
Minimum2024-03-10 00:00:00
Maximum2024-04-06 00:00:00
2024-07-25T18:43:14.947071image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:15.096351image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)

carrier
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
JetBlue
36156 
United
28364 
American
26802 
Delta
25562 
Spirit
9560 
Other values (4)
14002 

Length

Max length20
Median length11
Mean length6.6486906
Min length5

Characters and Unicode

Total characters933782
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpirit
2nd rowAlaska
3rd rowJetBlue
4th rowUnited
5th rowSpirit

Common Values

ValueCountFrequency (%)
JetBlue 36156
25.7%
United 28364
20.2%
American 26802
19.1%
Delta 25562
18.2%
Spirit 9560
 
6.8%
Alaska 8255
 
5.9%
Third Party 4098
 
2.9%
Frontier 1389
 
1.0%
Sun Country Airlines 260
 
0.2%

Length

2024-07-25T18:43:15.237844image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T18:43:15.370872image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
jetblue 36156
24.9%
united 28364
19.6%
american 26802
18.5%
delta 25562
17.6%
spirit 9560
 
6.6%
alaska 8255
 
5.7%
third 4098
 
2.8%
party 4098
 
2.8%
frontier 1389
 
1.0%
sun 260
 
0.2%
Other values (2) 520
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 154689
16.6%
t 105389
11.3%
i 80293
 
8.6%
a 72972
 
7.8%
l 70233
 
7.5%
n 57335
 
6.1%
r 47856
 
5.1%
u 36676
 
3.9%
J 36156
 
3.9%
B 36156
 
3.9%
Other values (18) 236027
25.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 933782
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 154689
16.6%
t 105389
11.3%
i 80293
 
8.6%
a 72972
 
7.8%
l 70233
 
7.5%
n 57335
 
6.1%
r 47856
 
5.1%
u 36676
 
3.9%
J 36156
 
3.9%
B 36156
 
3.9%
Other values (18) 236027
25.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 933782
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 154689
16.6%
t 105389
11.3%
i 80293
 
8.6%
a 72972
 
7.8%
l 70233
 
7.5%
n 57335
 
6.1%
r 47856
 
5.1%
u 36676
 
3.9%
J 36156
 
3.9%
B 36156
 
3.9%
Other values (18) 236027
25.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 933782
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 154689
16.6%
t 105389
11.3%
i 80293
 
8.6%
a 72972
 
7.8%
l 70233
 
7.5%
n 57335
 
6.1%
r 47856
 
5.1%
u 36676
 
3.9%
J 36156
 
3.9%
B 36156
 
3.9%
Other values (18) 236027
25.3%

Trip_Type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
Rounds Trip
77130 
One Way
63316 

Length

Max length11
Median length11
Mean length9.1967162
Min length7

Characters and Unicode

Total characters1291642
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRounds Trip
2nd rowRounds Trip
3rd rowRounds Trip
4th rowRounds Trip
5th rowRounds Trip

Common Values

ValueCountFrequency (%)
Rounds Trip 77130
54.9%
One Way 63316
45.1%

Length

2024-07-25T18:43:15.556037image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T18:43:15.654250image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
rounds 77130
27.5%
trip 77130
27.5%
one 63316
22.5%
way 63316
22.5%

Most occurring characters

ValueCountFrequency (%)
n 140446
 
10.9%
140446
 
10.9%
R 77130
 
6.0%
o 77130
 
6.0%
u 77130
 
6.0%
d 77130
 
6.0%
s 77130
 
6.0%
T 77130
 
6.0%
r 77130
 
6.0%
i 77130
 
6.0%
Other values (6) 393710
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1291642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 140446
 
10.9%
140446
 
10.9%
R 77130
 
6.0%
o 77130
 
6.0%
u 77130
 
6.0%
d 77130
 
6.0%
s 77130
 
6.0%
T 77130
 
6.0%
r 77130
 
6.0%
i 77130
 
6.0%
Other values (6) 393710
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1291642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 140446
 
10.9%
140446
 
10.9%
R 77130
 
6.0%
o 77130
 
6.0%
u 77130
 
6.0%
d 77130
 
6.0%
s 77130
 
6.0%
T 77130
 
6.0%
r 77130
 
6.0%
i 77130
 
6.0%
Other values (6) 393710
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1291642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 140446
 
10.9%
140446
 
10.9%
R 77130
 
6.0%
o 77130
 
6.0%
u 77130
 
6.0%
d 77130
 
6.0%
s 77130
 
6.0%
T 77130
 
6.0%
r 77130
 
6.0%
i 77130
 
6.0%
Other values (6) 393710
30.5%

Airport_Route
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
JFK - LAX
38780 
LAX - JFK
35535 
EWR - LAX
27505 
LAX - EWR
27396 
LAX - LGA
6315 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1264014
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEWR - LAX
2nd rowEWR - LAX
3rd rowEWR - LAX
4th rowEWR - LAX
5th rowLGA - LAX

Common Values

ValueCountFrequency (%)
JFK - LAX 38780
27.6%
LAX - JFK 35535
25.3%
EWR - LAX 27505
19.6%
LAX - EWR 27396
19.5%
LAX - LGA 6315
 
4.5%
LGA - LAX 4915
 
3.5%

Length

2024-07-25T18:43:15.764152image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T18:43:15.876117image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
140446
33.3%
lax 140446
33.3%
jfk 74315
17.6%
ewr 54901
 
13.0%
lga 11230
 
2.7%

Most occurring characters

ValueCountFrequency (%)
280892
22.2%
L 151676
12.0%
A 151676
12.0%
- 140446
11.1%
X 140446
11.1%
J 74315
 
5.9%
F 74315
 
5.9%
K 74315
 
5.9%
E 54901
 
4.3%
W 54901
 
4.3%
Other values (2) 66131
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1264014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
280892
22.2%
L 151676
12.0%
A 151676
12.0%
- 140446
11.1%
X 140446
11.1%
J 74315
 
5.9%
F 74315
 
5.9%
K 74315
 
5.9%
E 54901
 
4.3%
W 54901
 
4.3%
Other values (2) 66131
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1264014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
280892
22.2%
L 151676
12.0%
A 151676
12.0%
- 140446
11.1%
X 140446
11.1%
J 74315
 
5.9%
F 74315
 
5.9%
K 74315
 
5.9%
E 54901
 
4.3%
W 54901
 
4.3%
Other values (2) 66131
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1264014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
280892
22.2%
L 151676
12.0%
A 151676
12.0%
- 140446
11.1%
X 140446
11.1%
J 74315
 
5.9%
F 74315
 
5.9%
K 74315
 
5.9%
E 54901
 
4.3%
W 54901
 
4.3%
Other values (2) 66131
 
5.2%

price
Real number (ℝ)

HIGH CORRELATION 

Distinct1018
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean354.64997
Minimum46
Maximum4074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-07-25T18:43:16.020881image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile134
Q1229
median329
Q3452
95-th percentile655
Maximum4074
Range4028
Interquartile range (IQR)223

Descriptive statistics

Standard deviation174.14629
Coefficient of variation (CV)0.4910371
Kurtosis41.231725
Mean354.64997
Median Absolute Deviation (MAD)109
Skewness2.9817906
Sum49809169
Variance30326.93
MonotonicityNot monotonic
2024-07-25T18:43:16.737268image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204 2125
 
1.5%
229 2038
 
1.5%
244 2014
 
1.4%
274 1944
 
1.4%
254 1760
 
1.3%
259 1675
 
1.2%
319 1518
 
1.1%
309 1475
 
1.1%
178 1407
 
1.0%
374 1370
 
1.0%
Other values (1008) 123120
87.7%
ValueCountFrequency (%)
46 1
 
< 0.1%
47 1
 
< 0.1%
64 2
 
< 0.1%
65 2
 
< 0.1%
66 6
< 0.1%
71 13
< 0.1%
72 4
 
< 0.1%
73 7
< 0.1%
74 6
< 0.1%
75 1
 
< 0.1%
ValueCountFrequency (%)
4074 1
 
< 0.1%
4042 1
 
< 0.1%
4039 4
< 0.1%
4029 1
 
< 0.1%
3990 1
 
< 0.1%
3945 4
< 0.1%
3915 2
< 0.1%
3910 3
< 0.1%
3884 1
 
< 0.1%
3829 1
 
< 0.1%

overhead_bin
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
Additional charge for overhead bin
75344 
No additional charge for overhead bin
65102 

Length

Max length37
Median length34
Mean length35.390613
Min length34

Characters and Unicode

Total characters4970470
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdditional charge for overhead bin
2nd rowNo additional charge for overhead bin
3rd rowAdditional charge for overhead bin
4th rowAdditional charge for overhead bin
5th rowNo additional charge for overhead bin

Common Values

ValueCountFrequency (%)
Additional charge for overhead bin 75344
53.6%
No additional charge for overhead bin 65102
46.4%

Length

2024-07-25T18:43:16.853952image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T18:43:16.954253image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
additional 140446
18.3%
charge 140446
18.3%
for 140446
18.3%
overhead 140446
18.3%
bin 140446
18.3%
no 65102
8.5%

Most occurring characters

ValueCountFrequency (%)
626886
12.6%
o 486440
9.8%
a 486440
9.8%
i 421338
8.5%
d 421338
8.5%
r 421338
8.5%
e 421338
8.5%
n 280892
 
5.7%
h 280892
 
5.7%
c 140446
 
2.8%
Other values (8) 983122
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4970470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
626886
12.6%
o 486440
9.8%
a 486440
9.8%
i 421338
8.5%
d 421338
8.5%
r 421338
8.5%
e 421338
8.5%
n 280892
 
5.7%
h 280892
 
5.7%
c 140446
 
2.8%
Other values (8) 983122
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4970470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
626886
12.6%
o 486440
9.8%
a 486440
9.8%
i 421338
8.5%
d 421338
8.5%
r 421338
8.5%
e 421338
8.5%
n 280892
 
5.7%
h 280892
 
5.7%
c 140446
 
2.8%
Other values (8) 983122
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4970470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
626886
12.6%
o 486440
9.8%
a 486440
9.8%
i 421338
8.5%
d 421338
8.5%
r 421338
8.5%
e 421338
8.5%
n 280892
 
5.7%
h 280892
 
5.7%
c 140446
 
2.8%
Other values (8) 983122
19.8%

layover_count
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
0.0
121405 
1.0
17004 
2.0
 
1897
3.0
 
140

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters421338
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 121405
86.4%
1.0 17004
 
12.1%
2.0 1897
 
1.4%
3.0 140
 
0.1%

Length

2024-07-25T18:43:17.037297image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T18:43:17.130710image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 121405
86.4%
1.0 17004
 
12.1%
2.0 1897
 
1.4%
3.0 140
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 261851
62.1%
. 140446
33.3%
1 17004
 
4.0%
2 1897
 
0.5%
3 140
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 421338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 261851
62.1%
. 140446
33.3%
1 17004
 
4.0%
2 1897
 
0.5%
3 140
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 421338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 261851
62.1%
. 140446
33.3%
1 17004
 
4.0%
2 1897
 
0.5%
3 140
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 421338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 261851
62.1%
. 140446
33.3%
1 17004
 
4.0%
2 1897
 
0.5%
3 140
 
< 0.1%

round_trip_duration
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.913732
Minimum0
Maximum90
Zeros63316
Zeros (%)45.1%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-07-25T18:43:17.253677image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9
Q349
95-th percentile83
Maximum90
Range90
Interquartile range (IQR)49

Descriptive statistics

Standard deviation29.697849
Coefficient of variation (CV)1.1920273
Kurtosis-0.84034394
Mean24.913732
Median Absolute Deviation (MAD)9
Skewness0.79612442
Sum3499034
Variance881.96221
MonotonicityNot monotonic
2024-07-25T18:43:17.393954image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63316
45.1%
62 1405
 
1.0%
44 1346
 
1.0%
77 1246
 
0.9%
11 1246
 
0.9%
31 1238
 
0.9%
45 1236
 
0.9%
75 1194
 
0.9%
65 1181
 
0.8%
30 1176
 
0.8%
Other values (81) 65862
46.9%
ValueCountFrequency (%)
0 63316
45.1%
1 685
 
0.5%
2 861
 
0.6%
3 833
 
0.6%
4 745
 
0.5%
5 910
 
0.6%
6 869
 
0.6%
7 605
 
0.4%
8 1069
 
0.8%
9 871
 
0.6%
ValueCountFrequency (%)
90 860
0.6%
89 813
0.6%
88 1133
0.8%
87 956
0.7%
86 725
0.5%
85 837
0.6%
84 999
0.7%
83 885
0.6%
82 880
0.6%
81 924
0.7%

Carbon emissions estimate num
Real number (ℝ)

HIGH CORRELATION 

Distinct305
Distinct (%)0.2%
Missing693
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean371.9604
Minimum210
Maximum984
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 MiB
2024-07-25T18:43:17.521075image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum210
5-th percentile250
Q1297
median381
Q3411
95-th percentile538
Maximum984
Range774
Interquartile range (IQR)114

Descriptive statistics

Standard deviation92.604006
Coefficient of variation (CV)0.248962
Kurtosis-0.31711541
Mean371.9604
Median Absolute Deviation (MAD)74
Skewness0.59948433
Sum51982582
Variance8575.5019
MonotonicityNot monotonic
2024-07-25T18:43:17.670293image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
411 6928
 
4.9%
390 5178
 
3.7%
381 4675
 
3.3%
538 4276
 
3.0%
529 3606
 
2.6%
402 3556
 
2.5%
301 3508
 
2.5%
548 3499
 
2.5%
537 3307
 
2.4%
392 3012
 
2.1%
Other values (295) 98208
69.9%
ValueCountFrequency (%)
210 174
 
0.1%
215 210
 
0.1%
236 270
 
0.2%
238 654
0.5%
239 711
0.5%
240 9
 
< 0.1%
241 513
0.4%
242 867
0.6%
244 162
 
0.1%
245 61
 
< 0.1%
ValueCountFrequency (%)
984 2
 
< 0.1%
946 1
 
< 0.1%
835 10
 
< 0.1%
822 22
< 0.1%
817 47
< 0.1%
805 9
 
< 0.1%
803 1
 
< 0.1%
799 3
 
< 0.1%
716 1
 
< 0.1%
713 3
 
< 0.1%

carbon_emission% num
Real number (ℝ)

HIGH CORRELATION 

Distinct195
Distinct (%)0.1%
Missing91
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean112.98664
Minimum59
Maximum2522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 MiB
2024-07-25T18:43:17.809941image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile70
Q184
median107
Q3115
95-th percentile152
Maximum2522
Range2463
Interquartile range (IQR)31

Descriptive statistics

Standard deviation133.82932
Coefficient of variation (CV)1.1844703
Kurtosis219.71349
Mean112.98664
Median Absolute Deviation (MAD)21
Skewness14.542021
Sum15858240
Variance17910.288
MonotonicityNot monotonic
2024-07-25T18:43:17.948084image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 9760
 
6.9%
150 8084
 
5.8%
114 7601
 
5.4%
113 6992
 
5.0%
108 6437
 
4.6%
100 5933
 
4.2%
112 4391
 
3.1%
109 4207
 
3.0%
107 3665
 
2.6%
153 3555
 
2.5%
Other values (185) 79730
56.8%
ValueCountFrequency (%)
59 144
 
0.1%
60 240
 
0.2%
66 99
 
0.1%
67 1334
0.9%
68 1795
1.3%
69 1385
1.0%
70 2727
1.9%
71 3005
2.1%
72 3207
2.3%
73 2879
2.0%
ValueCountFrequency (%)
2522 3
 
< 0.1%
2519 1
 
< 0.1%
2515 1
 
< 0.1%
2510 2
 
< 0.1%
2508 1
 
< 0.1%
2505 1
 
< 0.1%
2503 2
 
< 0.1%
2501 4
 
< 0.1%
2498 15
< 0.1%
2496 1
 
< 0.1%

Days_to_Fly
Real number (ℝ)

Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.499096
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-07-25T18:43:18.085414image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q124
median45
Q367
95-th percentile86
Maximum90
Range89
Interquartile range (IQR)43

Descriptive statistics

Standard deviation25.606896
Coefficient of variation (CV)0.56280011
Kurtosis-1.1623426
Mean45.499096
Median Absolute Deviation (MAD)22
Skewness0.015860365
Sum6390166
Variance655.71312
MonotonicityNot monotonic
2024-07-25T18:43:18.211367image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 2012
 
1.4%
44 1961
 
1.4%
14 1904
 
1.4%
35 1892
 
1.3%
52 1886
 
1.3%
39 1873
 
1.3%
16 1865
 
1.3%
51 1841
 
1.3%
87 1836
 
1.3%
59 1826
 
1.3%
Other values (80) 121550
86.5%
ValueCountFrequency (%)
1 1306
0.9%
2 1147
0.8%
3 1392
1.0%
4 1613
1.1%
5 1496
1.1%
6 1329
0.9%
7 1684
1.2%
8 1319
0.9%
9 1822
1.3%
10 1512
1.1%
ValueCountFrequency (%)
90 1527
1.1%
89 1812
1.3%
88 1106
0.8%
87 1836
1.3%
86 1756
1.3%
85 1232
0.9%
84 1323
0.9%
83 1565
1.1%
82 1657
1.2%
81 1504
1.1%
Distinct12315
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
Minimum2024-03-12 05:01:00
Maximum2024-07-05 21:40:00
2024-07-25T18:43:18.353737image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:18.487661image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct16185
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
Minimum2024-03-12 08:16:00
Maximum2024-07-06 00:49:00
2024-07-25T18:43:18.637653image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:18.797035image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
3. 6 AM - 9 AM
35282 
4. 9 AM - 12 PM
24125 
8. 9 PM - 12 AM
23951 
6. 3 PM - 6 PM
22353 
5. 12 PM - 3 PM
18021 
Other values (3)
16714 

Length

Max length15
Median length14
Mean length14.474481
Min length14

Characters and Unicode

Total characters2032883
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3. 6 AM - 9 AM
2nd row3. 6 AM - 9 AM
3rd row2. 3 AM - 6 AM
4th row5. 12 PM - 3 PM
5th row6. 3 PM - 6 PM

Common Values

ValueCountFrequency (%)
3. 6 AM - 9 AM 35282
25.1%
4. 9 AM - 12 PM 24125
17.2%
8. 9 PM - 12 AM 23951
17.1%
6. 3 PM - 6 PM 22353
15.9%
5. 12 PM - 3 PM 18021
12.8%
7. 6 PM - 9 PM 14492
10.3%
2. 3 AM - 6 AM 1680
 
1.2%
1. 12 AM - 3 AM 542
 
0.4%

Length

2024-07-25T18:43:18.920654image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T18:43:19.055243image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
pm 157808
18.7%
140446
16.7%
am 123084
14.6%
9 97850
11.6%
6 96160
11.4%
3 77878
9.2%
12 66639
7.9%
4 24125
 
2.9%
8 23951
 
2.8%
5 18021
 
2.1%
Other values (3) 16714
 
2.0%

Most occurring characters

ValueCountFrequency (%)
702230
34.5%
M 280892
 
13.8%
P 157808
 
7.8%
. 140446
 
6.9%
- 140446
 
6.9%
A 123084
 
6.1%
9 97850
 
4.8%
6 96160
 
4.7%
3 77878
 
3.8%
2 68319
 
3.4%
Other values (5) 147770
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2032883
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
702230
34.5%
M 280892
 
13.8%
P 157808
 
7.8%
. 140446
 
6.9%
- 140446
 
6.9%
A 123084
 
6.1%
9 97850
 
4.8%
6 96160
 
4.7%
3 77878
 
3.8%
2 68319
 
3.4%
Other values (5) 147770
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2032883
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
702230
34.5%
M 280892
 
13.8%
P 157808
 
7.8%
. 140446
 
6.9%
- 140446
 
6.9%
A 123084
 
6.1%
9 97850
 
4.8%
6 96160
 
4.7%
3 77878
 
3.8%
2 68319
 
3.4%
Other values (5) 147770
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2032883
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
702230
34.5%
M 280892
 
13.8%
P 157808
 
7.8%
. 140446
 
6.9%
- 140446
 
6.9%
A 123084
 
6.1%
9 97850
 
4.8%
6 96160
 
4.7%
3 77878
 
3.8%
2 68319
 
3.4%
Other values (5) 147770
 
7.3%

to_hour_segment
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
7. 6 PM - 9 PM
30222 
8. 9 PM - 12 AM
26854 
4. 9 AM - 12 PM
21831 
6. 3 PM - 6 PM
18310 
5. 12 PM - 3 PM
15268 
Other values (3)
27961 

Length

Max length15
Median length15
Mean length14.517743
Min length14

Characters and Unicode

Total characters2038959
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4. 9 AM - 12 PM
2nd row4. 9 AM - 12 PM
3rd row3. 6 AM - 9 AM
4th row6. 3 PM - 6 PM
5th row6. 3 PM - 6 PM

Common Values

ValueCountFrequency (%)
7. 6 PM - 9 PM 30222
21.5%
8. 9 PM - 12 AM 26854
19.1%
4. 9 AM - 12 PM 21831
15.5%
6. 3 PM - 6 PM 18310
13.0%
5. 12 PM - 3 PM 15268
10.9%
3. 6 AM - 9 AM 12908
9.2%
1. 12 AM - 3 AM 8762
 
6.2%
2. 3 AM - 6 AM 6291
 
4.5%

Length

2024-07-25T18:43:19.213258image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T18:43:19.401348image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
pm 176285
20.9%
140446
16.7%
am 104607
12.4%
9 91815
10.9%
6 86041
10.2%
12 72715
8.6%
3 61539
 
7.3%
7 30222
 
3.6%
8 26854
 
3.2%
4 21831
 
2.6%
Other values (3) 30321
 
3.6%

Most occurring characters

ValueCountFrequency (%)
702230
34.4%
M 280892
 
13.8%
P 176285
 
8.6%
. 140446
 
6.9%
- 140446
 
6.9%
A 104607
 
5.1%
9 91815
 
4.5%
6 86041
 
4.2%
1 81477
 
4.0%
2 79006
 
3.9%
Other values (5) 155714
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2038959
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
702230
34.4%
M 280892
 
13.8%
P 176285
 
8.6%
. 140446
 
6.9%
- 140446
 
6.9%
A 104607
 
5.1%
9 91815
 
4.5%
6 86041
 
4.2%
1 81477
 
4.0%
2 79006
 
3.9%
Other values (5) 155714
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2038959
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
702230
34.4%
M 280892
 
13.8%
P 176285
 
8.6%
. 140446
 
6.9%
- 140446
 
6.9%
A 104607
 
5.1%
9 91815
 
4.5%
6 86041
 
4.2%
1 81477
 
4.0%
2 79006
 
3.9%
Other values (5) 155714
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2038959
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
702230
34.4%
M 280892
 
13.8%
P 176285
 
8.6%
. 140446
 
6.9%
- 140446
 
6.9%
A 104607
 
5.1%
9 91815
 
4.5%
6 86041
 
4.2%
1 81477
 
4.0%
2 79006
 
3.9%
Other values (5) 155714
 
7.6%

flight_duration_value
Real number (ℝ)

HIGH CORRELATION 

Distinct313
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5613688
Minimum2.5
Maximum46.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-07-25T18:43:19.555205image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile5.3
Q15.5
median6
Q36.3
95-th percentile10.5
Maximum46.9
Range44.4
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation2.7717993
Coefficient of variation (CV)0.42244223
Kurtosis41.818693
Mean6.5613688
Median Absolute Deviation (MAD)0.4
Skewness5.6967575
Sum921518
Variance7.6828712
MonotonicityNot monotonic
2024-07-25T18:43:19.681630image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.5 17245
12.3%
6.2 15646
11.1%
5.6 13884
9.9%
5.4 12767
 
9.1%
6 11455
 
8.2%
6.1 10509
 
7.5%
5.3 7052
 
5.0%
6.4 6563
 
4.7%
6.3 6019
 
4.3%
5.9 5285
 
3.8%
Other values (303) 34021
24.2%
ValueCountFrequency (%)
2.5 1
 
< 0.1%
3.8 1
 
< 0.1%
4.7 2
 
< 0.1%
4.9 155
 
0.1%
5 245
 
0.2%
5.1 1564
 
1.1%
5.2 3406
 
2.4%
5.3 7052
5.0%
5.4 12767
9.1%
5.5 17245
12.3%
ValueCountFrequency (%)
46.9 1
 
< 0.1%
43.5 4
< 0.1%
43.4 1
 
< 0.1%
42.7 1
 
< 0.1%
41.5 1
 
< 0.1%
41.2 2
 
< 0.1%
41 2
 
< 0.1%
40.9 6
< 0.1%
40.7 1
 
< 0.1%
40.3 2
 
< 0.1%

Holiday
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
Not_Holiday
137678 
Holiday
 
2768

Length

Max length11
Median length11
Mean length10.921165
Min length7

Characters and Unicode

Total characters1533834
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot_Holiday
2nd rowNot_Holiday
3rd rowNot_Holiday
4th rowNot_Holiday
5th rowNot_Holiday

Common Values

ValueCountFrequency (%)
Not_Holiday 137678
98.0%
Holiday 2768
 
2.0%

Length

2024-07-25T18:43:19.833531image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-25T18:43:19.937512image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
not_holiday 137678
98.0%
holiday 2768
 
2.0%

Most occurring characters

ValueCountFrequency (%)
o 278124
18.1%
H 140446
9.2%
l 140446
9.2%
i 140446
9.2%
d 140446
9.2%
a 140446
9.2%
y 140446
9.2%
N 137678
9.0%
t 137678
9.0%
_ 137678
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1533834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 278124
18.1%
H 140446
9.2%
l 140446
9.2%
i 140446
9.2%
d 140446
9.2%
a 140446
9.2%
y 140446
9.2%
N 137678
9.0%
t 137678
9.0%
_ 137678
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1533834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 278124
18.1%
H 140446
9.2%
l 140446
9.2%
i 140446
9.2%
d 140446
9.2%
a 140446
9.2%
y 140446
9.2%
N 137678
9.0%
t 137678
9.0%
_ 137678
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1533834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 278124
18.1%
H 140446
9.2%
l 140446
9.2%
i 140446
9.2%
d 140446
9.2%
a 140446
9.2%
y 140446
9.2%
N 137678
9.0%
t 137678
9.0%
_ 137678
9.0%

Fly_WeekDay
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9506358
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2024-07-25T18:43:20.020240image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9875377
Coefficient of variation (CV)0.50309313
Kurtosis-1.2089114
Mean3.9506358
Median Absolute Deviation (MAD)2
Skewness0.027130222
Sum554851
Variance3.9503062
MonotonicityNot monotonic
2024-07-25T18:43:20.121718image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 21934
15.6%
1 21058
15.0%
5 20776
14.8%
3 19939
14.2%
7 19590
13.9%
2 19416
13.8%
6 17733
12.6%
ValueCountFrequency (%)
1 21058
15.0%
2 19416
13.8%
3 19939
14.2%
4 21934
15.6%
5 20776
14.8%
6 17733
12.6%
7 19590
13.9%
ValueCountFrequency (%)
7 19590
13.9%
6 17733
12.6%
5 20776
14.8%
4 21934
15.6%
3 19939
14.2%
2 19416
13.8%
1 21058
15.0%

Interactions

2024-07-25T18:43:13.043068image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:08.037930image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:08.940473image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:09.677829image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:10.553826image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:11.497130image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:12.277577image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:13.170566image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:08.166691image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:09.041167image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:09.799814image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:10.683759image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:11.660650image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:12.394943image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:13.274524image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:08.269586image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:09.146580image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:09.910015image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:10.910672image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:11.769469image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:12.502023image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:13.398626image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:08.491359image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:09.249650image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:10.042324image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:11.062006image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:11.884339image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:12.628312image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:13.509467image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:08.601910image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:09.364552image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:10.156631image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:11.170941image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:11.980684image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:12.736275image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:13.604398image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:08.717010image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:09.462007image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:10.291888image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:11.273490image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:12.078185image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:12.836490image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:13.715061image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:08.836897image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:09.560286image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:10.408236image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:11.377332image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:12.167962image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-25T18:43:12.930741image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-07-25T18:43:20.236938image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Airport_RouteCarbon emissions estimate numDays_to_FlyFly_WeekDayHolidayTrip_Typecarbon_emission% numcarrierflight_duration_valuefrom_hour_segmentlayover_countoverhead_binpriceround_trip_durationto_hour_segment
Airport_Route1.0000.2250.0340.0360.0090.1020.1470.4490.3540.2370.4340.4050.1270.0450.277
Carbon emissions estimate num0.2251.000-0.1020.0070.0060.0780.9950.4530.0360.0930.2080.6980.1400.0260.096
Days_to_Fly0.034-0.1021.0000.0010.1560.042-0.1190.037-0.0380.0310.0650.065-0.003-0.0010.021
Fly_WeekDay0.0360.0070.0011.0000.2180.0490.0070.037-0.0240.0360.0370.0480.1130.0280.030
Holiday0.0090.0060.1560.2181.0000.0240.0070.0120.0120.0100.0080.0030.0170.0450.008
Trip_Type0.1020.0780.0420.0490.0241.0000.0180.0870.0610.0540.0600.0700.4930.8990.055
carbon_emission% num0.1470.995-0.1190.0070.0070.0181.0000.1840.0030.0760.0990.0610.1360.0280.067
carrier0.4490.4530.0370.0370.0120.0870.1841.0000.2750.1180.4170.9460.0990.0270.131
flight_duration_value0.3540.036-0.038-0.0240.0120.0610.0030.2751.0000.1020.6580.094-0.236-0.0530.087
from_hour_segment0.2370.0930.0310.0360.0100.0540.0760.1180.1021.0000.1400.1050.0480.0200.485
layover_count0.4340.2080.0650.0370.0080.0600.0990.4170.6580.1401.0000.0860.1240.0320.079
overhead_bin0.4050.6980.0650.0480.0030.0700.0610.9460.0940.1050.0861.0000.2310.0630.099
price0.1270.140-0.0030.1130.0170.4930.1360.099-0.2360.0480.1240.2311.0000.5950.052
round_trip_duration0.0450.026-0.0010.0280.0450.8990.0280.027-0.0530.0200.0320.0630.5951.0000.020
to_hour_segment0.2770.0960.0210.0300.0080.0550.0670.1310.0870.4850.0790.0990.0520.0201.000

Missing values

2024-07-25T18:43:13.888213image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-25T18:43:14.287835image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-25T18:43:14.670448image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Report_Run_TimecarrierTrip_TypeAirport_Routepriceoverhead_binlayover_countround_trip_durationCarbon emissions estimate numcarbon_emission% numDays_to_Flyfrom_timestamp_1to_timestamp_1from_hour_segmentto_hour_segmentflight_duration_valueHolidayFly_WeekDay
02024-03-10SpiritRounds TripEWR - LAX211.0Additional charge for overhead bin0.08929382182024-03-28 06:47:002024-03-28 10:15:003. 6 AM - 9 AM4. 9 AM - 12 PM6.5Not_Holiday4
12024-03-10AlaskaRounds TripEWR - LAX297.0No additional charge for overhead bin0.08927276182024-03-28 07:30:002024-03-28 10:48:003. 6 AM - 9 AM4. 9 AM - 12 PM6.3Not_Holiday4
22024-03-10JetBlueRounds TripEWR - LAX298.0Additional charge for overhead bin0.089422118182024-03-28 05:15:002024-03-28 08:30:002. 3 AM - 6 AM3. 6 AM - 9 AM6.2Not_Holiday4
32024-03-10UnitedRounds TripEWR - LAX302.0Additional charge for overhead bin0.089427119182024-03-28 12:00:002024-03-28 15:24:005. 12 PM - 3 PM6. 3 PM - 6 PM6.4Not_Holiday4
42024-03-10SpiritRounds TripLGA - LAX269.0No additional charge for overhead bin1.08933894182024-03-28 16:29:002024-03-28 17:00:006. 3 PM - 6 PM6. 3 PM - 6 PM27.5Not_Holiday4
52024-03-10Third PartyRounds TripEWR - LAX270.0Additional charge for overhead bin1.08929582182024-03-28 07:47:002024-03-28 12:54:003. 6 AM - 9 AM5. 12 PM - 3 PM8.1Not_Holiday4
62024-03-10Third PartyRounds TripLGA - LAX285.0Additional charge for overhead bin2.08931788182024-03-28 22:50:002024-03-29 11:03:008. 9 PM - 12 AM4. 9 AM - 12 PM15.2Not_Holiday4
72024-03-10UnitedRounds TripEWR - LAX309.0Additional charge for overhead bin0.08924669182024-03-28 07:00:002024-03-28 10:09:003. 6 AM - 9 AM4. 9 AM - 12 PM6.2Not_Holiday4
82024-03-10UnitedRounds TripEWR - LAX311.0Additional charge for overhead bin1.089410114182024-03-28 12:19:002024-03-28 17:28:005. 12 PM - 3 PM6. 3 PM - 6 PM8.2Not_Holiday4
92024-03-10UnitedRounds TripEWR - LAX320.0Additional charge for overhead bin0.08924669182024-03-28 08:25:002024-03-28 11:35:003. 6 AM - 9 AM4. 9 AM - 12 PM6.2Not_Holiday4
Report_Run_TimecarrierTrip_TypeAirport_Routepriceoverhead_binlayover_countround_trip_durationCarbon emissions estimate numcarbon_emission% numDays_to_Flyfrom_timestamp_1to_timestamp_1from_hour_segmentto_hour_segmentflight_duration_valueHolidayFly_WeekDay
1404692024-04-06JetBlueOne WayLAX - JFK254.0Additional charge for overhead bin0.00372106892024-07-04 05:55:002024-07-05 14:30:002. 3 AM - 6 AM5. 12 PM - 3 PM5.6Holiday4
1404702024-04-06JetBlueOne WayLAX - JFK254.0Additional charge for overhead bin0.00372106892024-07-04 07:00:002024-07-05 15:41:003. 6 AM - 9 AM6. 3 PM - 6 PM5.7Holiday4
1404712024-04-06JetBlueOne WayLAX - JFK254.0Additional charge for overhead bin0.00372106892024-07-04 08:00:002024-07-05 16:34:003. 6 AM - 9 AM6. 3 PM - 6 PM5.6Holiday4
1404722024-04-06JetBlueOne WayLAX - JFK254.0Additional charge for overhead bin0.00372106892024-07-04 23:42:002024-07-05 08:15:008. 9 PM - 12 AM3. 6 AM - 9 AM5.6Holiday4
1404732024-04-06AlaskaOne WayLAX - EWR259.0No additional charge for overhead bin0.0030085892024-07-04 08:10:002024-07-05 16:49:003. 6 AM - 9 AM6. 3 PM - 6 PM5.6Holiday4
1404742024-04-06AlaskaOne WayLAX - EWR259.0No additional charge for overhead bin0.0030085892024-07-04 10:20:002024-07-05 18:59:004. 9 AM - 12 PM7. 6 PM - 9 PM5.6Holiday4
1404752024-04-06JetBlueOne WayLAX - EWR259.0Additional charge for overhead bin0.00370100892024-07-04 11:20:002024-07-05 19:49:004. 9 AM - 12 PM7. 6 PM - 9 PM5.5Holiday4
1404762024-04-06DeltaOne WayLAX - JFK274.0No additional charge for overhead bin0.0029885892024-07-04 06:00:002024-07-05 14:35:003. 6 AM - 9 AM5. 12 PM - 3 PM5.6Holiday4
1404772024-04-06DeltaOne WayLAX - JFK274.0No additional charge for overhead bin0.0029885892024-07-04 09:20:002024-07-05 18:15:004. 9 AM - 12 PM7. 6 PM - 9 PM5.9Holiday4
1404782024-04-06DeltaOne WayLAX - JFK274.0No additional charge for overhead bin0.0029885892024-07-04 16:00:002024-07-05 00:37:006. 3 PM - 6 PM1. 12 AM - 3 AM5.6Holiday4